Paper submission: 21 January, 2019 (NOTE: Due to the large number of
requests,
IEEE CIS has just approved to keep the CEC 2019 paper submission system open until 31 January 2019.
You are welcomed to update or submit new papers by this date.
)

Decision notification: 7 March, 2019

Camera ready paper due: 8 April, 2019

Notification of Presentation Format (oral or poster): 15 April, 2019

Early bird Registration Deadline: 8 April, 2019

Conference: 10 June, 2019

Note: all deadlines are 11:59pm Anywhere on earth.

11/2018

Calls

Call For Paper

Papers for IEEE CEC 2019 should be submitted electronically through the submission link (TBA),
and will be refereed by experts in the fields and ranked based on the criteria
of originality, significance, quality and clarity.

Call For Special Sessions

Special session proposals are invited to IEEE CEC 2019. Special session proposals should include
the title, aim and scope (including a list of main topics), and the names of the organizers of
the special session, together with a short biography of all organizers. A list of potential
contributors will be very helpful. All special sessions proposals should be submitted to the
Special Session Chair: Prof Chuan-Kang Ting (Email: ckting@pme.nthu.edu.tw).

Call For Tutorials

IEEE CEC 2019 solicits proposal for tutorials covering specific topics in Evolutionary
Computation. If you are interested in proposing a tutorial, would like to recommend someone who
might be interested, or have questions about tutorials, please contact the Tutorial Chair: Prof
Xiaodong Li (Email: xiaodong.li@rmit.edu.au).

Call For Competition

Competitions will be held as part of the Congress. Prospective competition organizers are invited
to submit their proposals to the Competition Chair: Dr Jialin Liu (Email: liujl@sustc.edu.cn).

Call For Workshops

Workshops will be held to provide participants with the opportunity to present and discuss novel
research ideas on active and emerging topics in Evolutionary Computation. Prospective workshop
organizers are invited to submit their proposals to the Workshop Chair: Dr Handing Wang
(Email: hdwang@xidian.edu.cn).

Early Bird Paper Registration

CEC 2019

Public Lecture

Chair Professor

Department of Computer Science and Engineering
Southern University of Science and Technology
and
CERCIA, School of Computer Science
University of Birmingham

Title: What Can Evolutionary Computation Do For You?

Time: 1 hour

Evolutionary computation refers to the study of computational systems that use ideas and
get inspirations from natural systems, especially biological systems.
Its primary goal is to develop more robust, reliable and self-adaptive computational
systems that help to tackle complex optimisation and learning problems
in the real world, from routing a fleet of trucks in a dynamically changing road
networks to scheduling a large team of software engineers to develop
a complex software system, from calibrating engines of cars to design artificial neural
networks for pattern recognition and prediction. This talk tries to
explain what evolutionary computation is, how it works and why it is interesting from
both scientific research's point of view and practical application's
point of view. Examples will be given throughout the talk to illustrate the potential
benefits (and weakness) of evolutionary computation.

Prof. Xin Yao (M’91-SM’96-F’03) is a Chair Professor
of Computer Science at the Southern University of
Science and Technology, Shenzhen, China, and a
Professor of Computer Science at the University of
Birmingham, UK. He is an IEEE Fellow, and a
Distinguished Lecturer of IEEE Computational Intelligence Society (CIS). His major
research interests
include evolutionary computation, ensemble learning, and their applications in software
engineering.
He has been working on multi-objective optimisation
since 2003, when he published a well-cited EMO’03
paper on many objective optimisation. His research won the 2001 IEEE Donald G. Fink
Prize Paper Award, 2010, 2016, and 2017 IEEE TRANSACTIONS
ON EVOLUTIONARY COMPUTATION Outstanding Paper Awards, 2010 BT
Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE
TRANSACTIONS ON NEURAL NETWORKS Outstanding Paper Award, and
many other best paper awards. He received the prestigious Royal Society
Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary
Computation Pioneer Award in 2013. He was the the President (2014-15)
of IEEE CIS, and the Editor-in-Chief (2003-08) of IEEE TRANSACTIONS
ON EVOLUTIONARY COMPUTATION.

Plenary Talks

Hisao Ishibuchi

Chair Professor

Department of Computer Science and Engineering
Southern University of Science and Technology

Time: 1 hour

Evolutionary multi-objective optimization (EMO) has been a hot research area since the
1990s. Optimization problems usually have multiple objectives even if they are
traditionally handled by single-objective optimization. The goal of this talk is to
clearly explain that EMO is still a young research area with a number of interesting and
promising research topics. First, past research trends are explained from a viewpoint of
the relation between test problems and EMO algorithms. Easy test problems with respect
to the convergence were mainly used in the 1990s. As a result, non-elitist EMO
algorithms were proposed together with sophisticated diversification mechanisms since
those test problems were difficult with respect to the diversification. In the 2000s,
elitist EMO algorithms based on the Pareto dominance relation were proposed to handle
difficult test problems with respect to the convergence. In the 2010s, a number of
many-objective algorithms were proposed using the framework of decomposition-based EMO
algorithms since many-objective test problems have regular Pareto fronts. Next, current
research trends in the design of EMO algorithms are explained, which are motivated by
test problems with irregular Pareto fronts, large-scale test problems, and expensive
test problems. Then, difficulties of fair performance comparison of EMO algorithms are
explained: Performance comparison results depend on various factors such as the
population size, available computation time, and reference point specifications in the
hypervolume and IGD indicators as well as the choice of test problems. Finally, some
promising future research directions are suggested, which include the implementation of
fair performance comparison, the design of any-time EMO algorithms with an unbounded
archive population, and the selection of a small number of candidate solutions as well
as applications to machine learning and artificial intelligence.

Prof. Hisao Ishibuchi received the BS and MS degrees from Kyoto
University in 1985 and 1987, respectively. In 1992, he received the Ph. D. degree from
Osaka Prefecture University where he was a professor since 1999. From April 2017, he is
with Department of Computer Science and Engineering, Southern University of Science and
Technology (SUSTech), Shenzhen, China as a Chair Professor. He received Best Paper
Awards from GECCO 2004, HIS-NCEI 2006, FUZZ-IEEE 2009, WAC 2010, SCIS & ISIS 2010,
FUZZ-IEEE 2011, ACIIDS 2015, GECCO 2017, 2018, and EMO 2019. He also received a 2007
JSPS (Japan Society for the Promotion of Science) Prize and a 2019 IEEE CIS Fuzzy
Systems Pioneer Award. He was the IEEE CIS Vice-President for Technical Activities
(2010-2013), an IEEE CIS Distinguished Lecturer (2015-2017), and the President of the
Japan Evolutionary Computation Society (2016-2018). Currently, he is the Editor-in-Chief
of IEEE Computational Intelligence Magazine (2014-2019) and an IEEE CIS AdCom member
(2014-2019). He is also an Associate Editor of IEEE TEVC, IEEE Access, and IEEE T-Cyb.
He is an IEEE Fellow. In 2018, he was selected in the “Recruitment Program of Global
Experts for Foreign Experts” known as the “Thousand Talents Program” in China.

Emma Hart

Professor

School of Computing
Edinburgh Napier University

Title: Towards the Autonomous Evolution of Robotic Ecosystems

Time: 1 hour

From its very beginnings, Evolutionary Computation has been used as a tool to evolve
artefacts, starting with the very first optimisation of a joint plate at the Technical
University of Berlin in 1965, quickly followed by evolution of the often-cited two-phase
nozzle in1968. Since then, advances in computing (CPU power, simulation engines),
materials science, and engineering (3D printing, automated assembly) have considerably
enhanced our ability to evolve artefacts: these range from design of functional objects
such as satellite antennae, through creative design of chairs and tables, to design of
active artefacts such as robots that dynamically sense and interact with their
surroundings.
In this talk, I will give a brief history of the evolution of artefacts, leading to
evolutionary robotics, and finally to a recent collaborative project that aims to
deliver a disruptive robotic technology in which robots are created, reproduce and
evolve in real-time and real space. The long-term vision is of a technology that enables
the evolution of entire autonomous robotic ecosystems that live and work for long
periods in challenging and dynamic environments without the need for direct human
oversight, e.g. in outer-space, or decommissioning a nuclear reactor. Rather than being
designed and manufactured, the morphologies and control processes of robots will emerge
as a result of evolutionary processes, continuously changing both their form and
behaviour over-time.

Prof. Emma Hart gained a 1st Class Honours Degree in Chemistry
from the University of Oxford,
followed by an MSc in Artificial Intelligence from the University of Edinburgh. Her PhD,
also from the University of Edinburgh, explored the use of immunology as an inspiration
for computing, examining a range of techniques applied to optimisation and data
classification problems.
She moved to Edinburgh Napier University in 2000 as a lecturer, and was promoted to a
Chair in 2008 in Natural Computation. She is active world-wide in the field of
Evolutionary Computation, for example as General Chair of PPSN 2016, and as a Track
Chair at GECCO for several years. She has given keynotes at EURO 2016 and UKCI 2015, as
well as invited talks and tutorials at many Universities and international conferences.
She is Editor-in-Chief of Evolutionary Computation (MIT Press) from January 2016 and an
elected member of the ACM SIGEVO Executive Board. She is also a member of the UK
Operations Research Society Research Panel.

Risto Miikkulainen

Professor

The University of Texas at Austin
and
Cognizant Technology Solutions

Title: Creative AI through Evolutionary Computation

Time: 1 hour

Last decade has seen tremendous progress in Artificial Intelligence
(AI). AI is now in the real world, powering applications that have a
large practical impact. Most of it is based on modeling what is
already known, e.g. predicting what the right classification of an
image or a language sequence might be. The next step for AI is
machine creativity, e.g. designing engineering solutions are more
complex, perform better, or at a lower cost than existing solutions.
Evolutionary computation is likely to play a central role in future
such AI. I will review several recent techniques and applications
where evolutionary creativity improves upon best human solutions,
including designing neural network architectures, web interfaces, and
growth recipes for agriculture.

Prof. Risto Miikkulainen is a Professor of Computer Science at
the
University of Texas at Austin and Associate VP of Evolutionary AI at
Cognizant. He received an M.S. in Engineering from Helsinki University
of Technology (now Aalto University) in 1986, and a Ph.D. in Computer
Science from UCLA in 1990. His current research focuses on methods
and applications of neuroevolution, as well as neural network models
of natural language processing and vision; he is an author of over 400
articles in these research areas. Risto is an IEEE Fellow, and his work
on neuroevolution has recently been recognized with the Gabor Award of
the International Neural Network Society and Outstanding Paper of the
Decade Award of the International Society for Artificial Life.

Plenary Industry Talks

Wei Cui

Co-founder/Chief Scientist

Squirrel AI Learning
by
Yixue Group

Title: How AI Makes Personalized Education Affordable to Every Family in China

Time: 1 hour

Yixue Squirrel AI is an AI driven adaptive education platform that is revolutionising the
education industry and expanding fast at multiple fronts. In this talk I will introduce
the AI techniques used inside of Squirrel AI and the product evaluation process. Key
components of the system will be explained, including a student model, a pedagogy model,
a domain model, and a prediction model. I will also cover the techniques used in
Squirrel AI such as Genetic Algorithms, Machine Learning, Information Theory, Bayesian
methods, Neural Networks, Graph theory and Probabilistic Graphical Model.

Dr. Wei Cui is a co-founder and Chief Scientist of Squirrel AI Learning by Yixue Group,
the leading AI + adaptive education innovator at the forefront of AI revolution.
Squirrel AI Learning has established more than 1,800 learning centres in China within 4
years, and is included in the TOP 20 Chinese AI Unicorn Companies in 2018.
Dr. Cui led the development of Squirrel AI intelligent adaptive learning system, the
pioneering AI-powered adaptive learning system for K-12 students in China, which has
been proved to achieve a better effect at teaching than expert human-teachers in a
series of certified human-vs-AI competitions.
Dr. Cui holds a PhD and was a postdoctoral fellow in artificial intelligence and
algorithmic trading. He has published over 20 peer-reviewed academic papers and articles
in areas of AI, agent-based modelling, complex adaptive system, quantitative finance and
AI education. Dr. Cui was awarded MIT Technology Review "35 Innovators Under 35 China”
in 2018.

By bike or kick scooter

The waterfront around Te Papa is bicycle and kick scooter friendly. Please don't bring them
inside the museum but park your bike or kick scooter at our racks – found behind Quake
Braker, near our main entrance. We offer a lock for your scooter for purchase at a cost
of $7.

By train

By car

Take the Aotea Quay exit when driving south into central Wellington along the SH1 motorway.
Continue along Waterloo, Customhouse, and Jervois Quays, which lead directly into Cable Street
and Te Papa’s convenient car park.